Algorithm-Data Driven Optimization of
Adaptive Communication Networks

Algorithm-Data Driven Optimization of
Adaptive Communication Networks

This paper is motivated by the emerging vision of an automated
and data-driven optimization of communication
networks, making it possible to fully exploit the flexibilities
offered by modern network technologies and heralding an
era of fast and self-adjusting networks. We build upon our
recent study of machine-learning approaches to (statically)
optimize resource allocations based on the data produced
by network algorithms in the past. We take our study a
crucial step further by considering dynamic scenarios: scenarios
where communication patterns can change over time.
In particular, we investigate network algorithms which learn
from the traffic distribution (the feature vector ), in order to
predict global network allocations (a multi-label problem).
As a case study, we consider a well-studied k-median problem
arising in Software-Defined Networks, and aim to imitate
and speedup existing heuristics as well as to predict good
initial solutions for local search algorithms. We compare different
machine learning algorithms by simulation and find
that neural network can provide the best abstraction, saving
up to two-thirds of the algorithm runtime.